A Knowledge-Based System for Intelligent Support in Pharmacogenomics Evidence Assessment: Ontology-Driven Evidence Representation and Retrieval 2017 Joint Summits on Translational Science | AMIA Tuesday, March 28, 2017 S17: Papers – Clinical Integration ChiaJu (Cheryl) Lee, PhD

Disclosure I disclose that I have no relevant financial relationships with commercial interests. 2

Pharmacogenomics (PGx) Background and Significance Precision Medicine Takes Individual Variation into Account Harmful but effective Harmful Pharmacogenomics (PGx) How genetic variants affect a person’s response to a drug Takes into account individual variability in genes For safe & effective medication prescribing Neither harmful nor effective Safe and effective Source: http://sites.google.com/site/genomicssok/home 3

The need for development of knowledge bases Background and Significance Widespread Integration of PGx in Everyday Clinical Practice Is Still Lacking Projects in support of preemptive genetic testing PREDICT1 The 1200 Patients2 PG4KDS3 RIGHT Protocol4 Insufficient evidence to recommend clinical validity and utility of a genetic testing5-8 The need for development of knowledge bases PharmGKB9, ClinVar10, ClinicalTrials.gov [1]PMID: 22588608, [2]PMID: 22929923, [3]PMID: 24619595, [4]PMID: 24388019 [5]PMID: 16013993, [6]PMID: 20981006, [7]PMID: 21884816, [8]PMID: 21619429 [9]PMID: 20350130, [10]PMID: 24234437 4

Research Hypothesis Proposal: Knowledge-Based System (KBS) for Providing Computable PGx Evidence With the rapid growth of genomic research, the availability of evidence may not be a major concern Real challenge is how to make effective use of existing study results to support timely decision making Hypothesis: a knowledge-based system to facilitate effective and efficient evidence assessment of PGx adoption 5

Improving the Systematic Review Pipeline Methods Improving the Systematic Review Pipeline Natural language processing, machine learning, text mining11-13 Conduct a literature search Screen to identify relevant studies Extract essential data from studies Synthesize the extracted data Rate the quality and strength of evidence Interpret the results Knowledge-based system (KBS) 6 [11]PMID: 20920176, [12]PMID: 25005128, [13]PMID: 26073888

Analogy between TINKERTOY® and Knowledge-Based System Methods Analogy between TINKERTOY® and Knowledge-Based System Constructs e.g., rod Constructors e.g., connector Source: http://www.knex.com/30-model-super-building-set The more complex construction demands more constructs and constructors 7

Knowledge-Based System Is Capable of Providing Reasoning Services Methods Knowledge-Based System Is Capable of Providing Reasoning Services Constructs i.e., classes, properties, instances Knowledge-Based System Knowledge Base Ontology (TBox) Knowledge Representation Language Reasoner formal representation reasoning Asserted Individuals (ABox) Constructors i.e., property restrictions, set operators Expressivity in representation vs. Efficiency in reasoning Applications Retrieval Classification Draw Inferences 8

Strategy to Develop a KBS de novo Methods Strategy to Develop a KBS de novo Conceptual modeling Describe a domain through a conceptual model Ontology development Convert the model to ontology constructs Ontology-based knowledge base development Encode asserted individuals by combining ontology constructs with constructors Evaluation Correctness in representation Efficiency in reasoning 9

Prototype PGx-KBS for Clopidogrel and Warfarin Methods Prototype PGx-KBS for Clopidogrel and Warfarin (2) Ontology development (1) Conceptual modeling (4) KBS evaluation (3) KB development A conceptual model composed of 9 information modules Individual P Individual S Individual E Publication Study Population Study Design Drug Therapy Risk of Bias Assessment Comparison Genetic Variation Outcome Effect Estimation 10

A Piece of Computable Individual Evidence Results A Piece of Computable Individual Evidence 7 constructs (3 classes, 4 properties), 2 constructors (some, and), 1 data value (i.e., 28) Outcome measure was the percentage of time of international normalized ratio (INR) in the therapeutic range up to the follow-up of 28 days14. [14]PMID: 24251361 11

Results Evaluation of Prototype PGx-KBS for Retrieval Effectiveness and Efficiency 73 computable Publications 82 computable Studies 445 computable Evidence 33 defined classes 33 IC [15]PMID: 22929815, [16]PMID: 22591668, [17]PMID: 21816733 [18]PMID: 21693476, [19]PMID: 20845077, [20]PMID: 20620727 [21]PMID: 20351750, [22]PMID: 22203539, [23]PMID: 22757746 SRs Singh15 Jang16 Bauer17 Zabalza18 Jin19 Hulot20 Sofi21 Holmes22 Yamaguchi23 # of MA included 9 6 4 3 2 1 # of E retrieved for all included MAs 57 58 44 31 19 22 23 16 Computing time (sec) 21 18 17 11 Precision 100% 11

Contributions Major Contributions Among the first to employ a KBS to improve efficiency, precision and transparency in the systematic review process Ontology for formal representation of PGx knowledge and inclusion criteria KB for accumulation of computable knowledge Reasoner for efficient retrieval Exploited the expressivity and reasoning ability of OWL 2 DL Advanced constructors of OWL 2 DL Benefit the evidence-based practice of genomics medicine 13

Major Limitations and Future Work Limitations & Future Work Major Limitations and Future Work The whole process is not seamless in practice No efforts have been undertaken to develop plug-ins to automatically export retrieval results from Protégé to existing statistical software that supports meta-analysis. Scope is limited to clinical validity and utility of clopidogrel and warfarin PGx Future Work: expand from germline to somatic/tumor PGx e.g., cancer PGx for targeted therapy selection 14

Acknowledgements Peter Tarczy-Hornoch, MD, FACMI Beth Devine, Pharm.D., MBA, PhD James Brinkley, MD, PhD, FACMI John Horn, Pharm.D., FCCP University of Washington Colleagues Precision Medicine Informatics Group This work was supported in part by U01 HG006507, Biomedical and Health Informatics Program and Pharmaceutical Outcomes Research & Policy Program 15

Thank you! Questions? cherylee@uw.edu 16